Like standard DenseNets, we can generalize this setup to contain multiple (\(N_c\) ) cells of width \(w_c\)  and depth \(d_c\) ; DTSC are present only within a cell and not across cells. Furthermore, we increase the width (i.e., the number of output channels per layer) by a factor of 2 and halve the height and width of the feature map cell by cell, following the standard practice  [1]}. After several cells (groups) of convolutions layers, the final feature map is average-pooled and passed through a fully-connected layer to generate the logits. The width of each cell is controlled using a width multiplier, \(w_m\)  (like in Wide-ResNets [2]}). The base number of channels of each cell is [16,32,64]. For \(w_m = 3\) , cells will have [48,96,192] channels per layer. To summarize, we control the value \(\lbrace w_m,N_c,d_c,t_{c}\rbrace \)  to sample candidate architectures from the entire search space.
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